Toward Drift-Aware Database Benchmarking

Data and workload drift are critical to evaluating core database components such as caching, cardinality estimation, indexing, and query optimization, especially as AI-driven techniques increasingly permeate database systems. However, existing benchmarks remain largely static, offering little support for modeling drifts. This limitation arises from the absence of a shared vocabulary and practical tools for specifying and generating drift in both data and workloads.

Guided by this vision of making drift a first-class concept, we propose a taxonomy of data and workload drift and design DriftSpec , a declarative specification that makes these drifts executable. Building on this, we present DriftBench , which instantiates DriftSpec to generate controlled drifts and enable drift-aware benchmarking. Together, the taxonomy, DriftSpec, and DriftBench form a first step toward a standardized, executable language for studying how data and workload evolution influence database behavior. They shift benchmarking from static, one-off tests to controlled, continuous evaluation under drift.